Cluster head shuffling based global optimization using elephant herd optimization (EHO) approach

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Wireless Sensors are susceptible from frequent energy decay which leads to the reduction of lifetime of entire network scenario. Such energy loss occurring in the sensor nodes are addressed and worked out by number of researchers using number of methods including Low-energy adaptive clustering hierarchy (LEACH) and its number of variants. Despite of enormous variants of LEACH, there is still huge scope of research because of increasing use of sensor nodes in assorted scenarios. The development of energy aware wireless sensor networks is in research from a long time because of the increasing issues related to lesser lifetime of nodes in the wireless environment. The traditional lifetime of wireless nodes even in smart grids is 835 days while the other wireless nodes die in maximum 30 days. Many times, the battery time of wireless sensor nodes is very few days which is a costly affair. It is difficult and cost consuming to redeploy the wireless nodes to reform the network and cost of clustering. In this research work, a novel and performance aware approach Elephant Herd Optimization based Cluster Head Selection is developed and implemented so that the optimization level can be improved. The nature inspired soft computing approaches are prominently used for global optimization and reduction of error factors from existing results and that is the key focus in this research work.


  • Keywords


    Cluster Head Selection in Wireless Networks, Energy Optimization, Elephant Herd Optimization, Wireless Sensor Networks

  • References


      [1] Tse D, Viswanath P. Fundamentals of wireless communication. Cambridge university press; 2005 May 26.

      [2] Kakkar A, Singh DM, Bansal DP. Efficient key mechanisms in multinode network for secured data transmission. International Journal of Engineering Science and Technology. 2010;2(5):787-95.

      [3] Camp T, Rubin MJ, Gonzalez S. Challenges in Developing Intelligent Geosystems (and the pros/cons of interdisciplinary research). InComputing, Networking and Communications (ICNC), 2015 International Conference on 2015 Feb 16 (pp. 591-597). IEEE.

      [4] Hartenstein H, Laberteaux LP. A tutorial survey on vehicular ad hoc networks. IEEE Communications magazine. 2008 Jun;46(6).

      [5] Anand J, Sivachandar K. Vampire Attack Detection in Wireless Sensor Network. International Journal of Engineering Science and Innovative Technology (IJESIT) Volume. 2014 Jul;3.

      [6] Ghasemzadeh R, Latif A. Improving LEACH Protocol Using SFLA Algorithm to Reduce the Energy Consumption of Wireless Sensor Networks. International Journal of Scientific Engineering and Technology. 2017;6(7):255-9.

      [7] Reddy GR, Balaji S. A Review on Different Types of LEACH Protocol for Wireless Sensor Networks.

      [8] Naranjo PG, Shojafar M, Mostafaei H, Pooranian Z, Baccarelli E. P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks. The Journal of Supercomputing. 2017 Feb 1;73(2):733-55.

      [9] Miglani A, Bhatia T, Sharma G, Shrivastava G. An Energy Efficient and Trust Aware Framework for Secure Routing in LEACH for Wireless Sensor Networks. Scalable Computing: Practice and Experience. 2017 Sep 9;18(3):207-18.

      [10] Vijayan K, Raaza A. A novel cluster arrangement energy efficient routing protocol for wireless sensor networks. Indian Journal of science and Technology. 2016 Feb 5;9(2).

      [11] Alasadi HA. Energy Efficient Hierarchical Clustering Mechanism for Wireless Sensor Network Fields. International Journal of Computer Applications. 2016;153(8).

      [12] Tahir M, Khan F, Jan SR, Azim N, Khan IA, Ullah F. EEC: Evaluation of Energy Consumption in Wireless Sensor Networks. International Journal of Engineering Trends and Applications, ISSN. 2016:2393-9516.

      [13] SOLAIMAN B. Energy optimization in wireless sensor networks using a hybrid k-means pso clustering algorithm. Turkish Journal of Electrical Engineering & Computer Sciences. 2016 Apr 15;24(4):2679-95.

      [14] Bouyer A, Hatamlou A, Masdari M. A new approach for decreasing energy in wireless sensor networks with hybrid LEACH protocol and fuzzy C-means algorithm. International Journal of Communication Networks and Distributed Systems. 2015;14(4):400-12.

      [15] Arumugam GS, Ponnuchamy T. EE-LEACH: development of energy-efficient LEACH Protocol for data gathering in WSN. EURASIP Journal on Wireless Communications and Networking. 2015 Mar 20;2015(1):76.

      [16] Mehmood A, Mauri JL, Noman M, Song H. Improvement of the Wireless Sensor Network Lifetime Using LEACH with Vice-Cluster Head. Ad Hoc & Sensor Wireless Networks. 2015 Aug;28(1-2):1-7.

      [17] Khiati M, Djenouri D. BOD‐LEACH: broadcasting over duty‐cycled radio using LEACH clustering for delay/power efficient dissimilation in wireless sensor networks. International Journal of Communication Systems. 2015 Jan 25;28(2):296-308.


 

View

Download

Article ID: 10039
 
DOI: 10.14419/ijet.v7i2.4.10039




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.